cicyt UNIZAR
Full-text links:

Download:

Current browse context:

q-fin.MF

Change to browse by:

References & Citations

Bookmark

(what is this?)
CiteULike logo BibSonomy logo Mendeley logo del.icio.us logo Digg logo Reddit logo ScienceWISE logo

Quantitative Finance > Mathematical Finance

Title: Stock Price Prediction using Principle Components

Abstract: The literature provides strong evidence that stock prices can be predicted from past price data. Principal component analysis (PCA) is a widely used mathematical technique for dimensionality reduction and analysis of data by identifying a small number of principal components to explain the variation found in a data set. In this paper, we describe a general method for stock price prediction using covariance information, in terms of a dimension reduction operation based on principle component analysis. Projecting the noisy observation onto a principle subspace leads to a well-conditioned problem. We illustrate our method on daily stock price values for five companies in different industries. We investigate the results based on mean squared error and directional change statistic of prediction, as measures of performance, and volatility of prediction as a measure of risk.
Comments: 28 Pages, 10 figures
Subjects: Mathematical Finance (q-fin.MF)
Cite as: arXiv:1803.05075 [q-fin.MF]
  (or arXiv:1803.05075v1 [q-fin.MF] for this version)

Submission history

From: Mahsa Ghorbani [view email]
[v1] Tue, 13 Mar 2018 23:34:26 GMT (62kb,D)